This invention relates to the management of oil or gas reservoirs, and more particularly, to the analysis of the production of petroleum reservoirs.
A petroleum reservoir is a zone in the earth that contains, or is thought to contain, one or more sources of commercially viable quantities of recoverable oil or gas. When such a reservoir is found, typically one or more wells are drilled into the earth to tap into the source(s) of oil or gas for producing them to the surface.
The art and science of managing petroleum reservoirs has progressed over the years. Various techniques have been used for trying to determine if sufficient oil or gas is in the given reservoir to warrant drilling, and if so, how best to develop the reservoir to produce the oil or gas that is actually found.
Every reservoir is unique because of the myriad of geological and fluid dynamic characteristics. Thus, the production of petroleum from reservoir to reservoir can vary drastically. These variations make it difficult to simply predict the amount of fluids and gases a reservoir will produce and the amount of resources it will require to produce from a particular reservoir. However, parties which are interested in producing from a reservoir need to project the production of the reservoir with some accuracy in order to determine the feasibility of producing from the reservoir. Therefore, in order to accurately forecast production rates from all of the wells in a reservoir, it is necessary to build a detailed computer model of the reservoir.
Prior art computer analysis of production for an oil reservoir is usually divided into two phases, history matching and prediction.
When an oil field is first discovered, a reservoir model is constructed utilizing geological data. Geological data can include such characteristics as the porosity and permeability of the reservoir rocks, the thickness of the geological zones, the location and characteristics of geological faults, and relative permeability and capillary pressure functions. This type of modeling is a forward modeling task and can be accomplished using statistical or soft computing methods. Once the petroleum field enters into the production stage, many changes take place in the reservoir. For example, the extraction of oil/gas/water from the field causes the fluid pressure of the field to change. In order to obtain the most current state of a reservoir, these changes need to be reflected in the model.
History matching is the process of updating reservoir descriptor parameters in a given computer model to reflect such changes, based on production data collected from the field. Production data essentially give the fluid dynamics of the field, examples include water, oil and pressure information, well locations and performances. Thus, reservoir models use empirically acquired data to describe a field. Input parameters are combined with and manipulated by mathematical models whose output describes specified characteristics of the field at a future time and in terms of measurable quantities such as the production or injection rates of individual wells and groups of wells, the bottom hole or tubing head pressure at each well, and the distribution of pressure and fluid phases within the reservoir.
In the history matching phase, geological data and production data of the reservoir and its wells are used to build a mathematical model which can predict production rates form wells in the reservoir. The process of history matching is an inverse problem. In this problem, a reservoir model is a “black box” with unknown parameter values. Given the water/oil rates and other production information collected from the field, the task is to identify these unknown parameter values such that the reservoir gives flow outputs matching the production data. Since inverse problems have no unique solutions, i.e., more than one combination of reservoir parameter values give the same flow outputs, a large number of well-matched or “good” reservoir models needs to be obtained in order to achieve a high degree of confidence in the history-matching results.
Initially, a base geological model is provided. Next, parameters which are believed to have an impact on the reservoir fluid flow are selected. Based on their knowledge about the field, geologists and petroleum engineers then determine the possible value ranges of these parameters and use these values to conduct computer simulation runs.
A computer reservoir simulator is a program which consists of mathematical equations that describe fluid dynamics of a reservoir under different conditions. The simulator takes a set of reservoir parameter values as inputs and returns a set of fluid flow information as outputs. The outputs are usually a time-series over a specified period of time. That time-series is then compared with the historical production data to evaluate their match. Experts modify the input parameters of the computer model involved in that particular simulation of the reservoir on the basis of the differences between computed and actual production performance and rerun the simulation of the computer model. This process continues until the computer or mathematical model behaves like the real oil reservoir.
The prior art manual process of history matching is subjective and labor-intensive, because the input reservoir parameters are adjusted one at a time to refine the computer simulations. The accuracy of the prior art history matching process largely depends on the experiences of the geoscientists involved in modifying the geological and production data. Consequently, the reliability of the forecasting is often very short-lives, and the business decisions made based on those models have a large degree of uncertainty.
As described-above, the prior art history matching process is very time consuming. On average, each run takes 2 to 10 hours to complete. Moreover, there can be more than one computer model with different input parameters which can produce flow outputs that are acceptable matches to the historical production data of the reservoir. This is particularly evident when the reservoir has a long production history and the quality of production data is poor. Determining which models can produce acceptable matches of the production data from a large pool of potentially acceptable computer models is cost prohibitive and time consuming. Because of those restrictions, only a small number of simulations can be run, and consequently only a small number of acceptable models are identified. As a result, the prior art history matching process is associated with a large degree of uncertainty as to the actual real world reservoir configuration. That large degree of uncertainty in the history matching phase also translates into a large degree of variability in the future production forecasts.
There is a need to identify large numbers of acceptable computer models in the history matching phase that are consistent with the geological data and the historical production data for a given reservoir. The facilitation of multiple realizations in history matching enables one to reduce the uncertainty in the reservoir models.
The second phase of the computer analysis of production for the oil reservoir is prediction or forecasting. Once an acceptable computer model has been identified, alternative operating plans of the reservoir are simulated and the results are compared to optimize the oil recovery and minimize the production costs. Because of the uncertainty in the reservoir model that has been generated from the prior art history matching process, any future production profile forecasted by that model also has a high degree of uncertainty associated with it.
In addition, as described-above, there are a number of computer models that have to be utilized in the prediction phase in order to reduce the uncertainty in the production forecasts. For each good model that was identified in the history matching phase, computer simulations are run to give a future production profile. In this manner, a range of production forecasts are determined and used to optimize the future production of the reservoir. As with the simulations in the history matching phase, the computer simulation phase is time consuming and requires a great deal of expertise which limits the number of acceptable computer models that can be used in the prior art prediction phase. There is a need to efficiently analyze large numbers of acceptable computer models which have been identified in the history matching phase of the analysis of production for the oil reservoir.
Even when experts are used in the analysis, there is much educated trial and error effort spent in choosing acceptable reservoir models in the history matching phase, running the simulations of the models, determining the optimal inputs for the models to predict future production forecasts, and analyzing the results from the models to determine the correct forecasts or a range of forecasts. This is time consuming and expensive, and it requires a highly skilled human expert to provide useful results.
If the potential pool of reservoir models in the history matching phase of the analysis is under-sampled, the uncertainty in the computer analysis of production for the reservoir will increase. There is, therefore, a need to sample and identify as many acceptable reservoir models in the history matching phase as possible to reduce the degree of uncertainty associated with the results of the computer analysis. There is also a need to be able to efficiently analyze those identified acceptable models and provide production forecasts for the reservoir.
The ability to more quickly and less expensively analyze a reservoir by whatever means is becoming increasingly important. Companies that develop oil or gas reservoirs are basing business decisions on entire reservoir analysis rather than just on individual wells in the field. Even after a field development plan is put into action, the computer analysis of production of the reservoir is periodically rerun and further tuned to improve the ability to match newly gathered production data. Because these decisions need to be made quickly as opportunities present themselves, there is the need for an improved method of analyzing petroleum reservoirs and, particularly, for accurately forecasting the oil and/or gas production of the reservoirs into the future.